Agents that resolve the request, start to finish.
The same questions come in every day, and someone answers each one by hand. We connect agents to the systems that hold the answers, like orders, accounts, stock and policies, so they can sort the request out instead of guessing. Every action is logged, and you decide when a person takes over.
What we design against
What a build in this line is scoped and measured against — not averages from past clients.
- 70%
- of repeat tickets resolved without a human
- <4s
- median first response
- 100%
- of tool calls audit-logged
- 01OpenAI / Anthropic
- 02Vector search
- 03Zendesk / Intercom
- 04Slack
- 05Postgres
The queue
When answering is the job nobody has time for.
Guardrails
What makes an agent safe to put in front of a customer.
Grounded in your data
Retrieval over your docs, policies, and product data — with citations, so an answer can be checked.
Real tool calls
Look up an order, issue a refund, reschedule a job. The agent acts inside guardrails you set.
Clean human handoff
Confidence thresholds and topic rules decide when a person takes over, with full context attached.
Evaluation harness
A test set of real conversations runs on every change, so quality is measured, not hoped for.
How we get there
From your real transcripts to an agent that closes tickets.
We read your real conversations and cluster what's actually being asked.
We split what an agent should close from what a person must own.
Tools, guardrails, and an eval set built from your own history.
Runs alongside your team first, then takes traffic as the numbers hold.
Try it
A real agent, not a recording.
not a recording — try a different message above
What teams ask before they let an agent reply.
Tell us what your queue looks like.
Send us a week of tickets and we'll tell you what share an agent could close — including if the answer is 'not enough to bother'.